TURNING A BLIND EYE? ON THE POLITICAL ECONOMY OF ENVIRONMENTAL REGULATION IN CHINA DALIA GHANEM. University of California, Davis SHU SHEN

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1 TURNING A BLIND EYE? ON THE POLITICAL ECONOMY OF ENVIRONMENTAL REGULATION IN CHINA DALIA GHANEM University of California, Davis SHU SHEN University of California, Davis JUNJIE ZHANG Duke Kunshan University and Duke University Abstract. This paper provides evidence on the political economy of environmental data manipulation in China. We use a unique panel data set of 111 cities between 2001 and The data set includes reported daily air pollutant concentrations as well as demographic, education and experience characteristics of city party secretaries and mayors. We develop an innovative censored MLE strategy to estimate the proportion of manipulated blue-sky days for each city/year. Then we use the LASSO shrinkage technique to find potential predictors of city-level manipulation patterns among a large set of party secretary and mayor characteristics. We find that city party secretaries with elite undergraduate degrees are associated with 2.38 additional manipulated blue-sky days in a year. Furthermore, among elite educated party secretaries, females and those with prior research experience are associated with lower levels of manipulation. No mayor characteristics are significant predictors of manipulation. The patterns in the data are consistent with economic growth being a possible explanation for increased manipulation under elite educated party secretaries. addresses: dghanem@ucdavis.edu, innoshu@gmail.com, junjie.zhang@duke.edu. Date: July 19, We would like to thank Kevin Novan for helpful discussions. Excellent research assistance by Xiaomeng Cui is greatly appreciated. We would also like to thank Kexin Liu, Qi Qin, Un Leong, Wanru He, Yiran Li, Zhenxuan Wang, and Shuo Li for aiding in the data collection process. 1

2 1. Introduction Environmental regulation is often compromised in China when government officials are facing competing political targets other than environmental quality. The worst-case scenario is when environmental compliance is achieved not by reducing emissions but by gaming the data. Issues with the accuracy of Chinese air pollution data were first noted in Andrews (2008a,b) and have been examined by various econometric techniques in Chen et al. (2012), Ghanem and Zhang (2014) and Fu et al. (2014). In particular, Ghanem and Zhang (2014) found evidence consistent with manipulation for about half of the reporting cities during However, all of the previous studies focus on the statistical significance of manipulation, i.e. examining whether there is data manipulation, rather than its economic significance. In this paper, we not only refine the method of identifying data manipulation but also provide a political economy interpretation of data falsification behavior in China, using a unique data set that combines reported air quality information with resumé details of city party secretaries and mayors. First of all, we propose a new econometric method to quantify the degree of data manipulation in the presence of a policy threshold. During , the Chinese central government used the number of blue-sky days, which are days with air pollution index (API) less than 101, as one of the performance measures to evaluate local officials. A city that meets the criterion of 80% or more blue-sky days in a year has a chance to be prized with the National Environmental Protection Model City Award (Chen et al., 2012). This naturally leads to an incentive to manipulate the API to be less than 101 as long as the manipulation is hard to detect. Using a data set of daily air quality for 111 cities between 2001 and 2010, we estimate the annual proportion of data manipulation around the blue-sky day cutoff via censored maximum likelihood estimation (MLE) using daily PM 10 concentrations for each city/year. This allows us to estimate the annual proportion of manipulation among blue-sky days as well as the number of manipulated blue-sky days 2

3 in a given year for each city. We find that the mean proportion of manipulation among blue-sky days is about 3.1% and the mean number of manipulated blue-sky days is about 8.5 in a year. Second, we try to explain the heterogeneous manipulation behavior by linking it to local officials characteristics and socioeconomic conditions. We compile a new panel dataset with detailed demographic, education, and work experience information of party secretaries and mayors in charge of those 111 cities between 2001 and We combine the panel with censored MLE estimates on manipulation behavior to examine the relationship between data falsification and local officials background characteristics. Given the large amount of information contained in the new panel, we use the least absolute shrinkage and selection operator (LASSO) to select key predictors of manipulation and to perform valid postselection inference. The most pronounced result we find is that a party secretary having an elite college degree is more likely to manipulate, which is robust to several checks. We specifically find that having an elite educated party secretary is associated with a statistically significant 1.1% increase in the proportion of manipulation among blue-sky days, which is about one-third of the mean proportion of manipulation among blue-sky days in our sample. Regarding manipulated blue-sky days, it is associated with an additional 2.38 such days in a year, also one-third of the mean number of manipulated blue-sky days in our sample. Elite education can capture several unobservable characteristics, such as ability, signaling, and connectedness, which are hard to separate in our data. When examining the heterogeneity in manipulation behavior among elite educated party secretaries, we found that those with previous research experience or previous work experience as a county mayor are associated with significantly less manipulation behavior than others. Because research experience and work experience as county mayor are both ability proxies of the local officials, the results 3

4 suggest that it is unlikely that the unobservable characteristic channeled through the elite education variable in the context of manipulation is ability. We find no evidence that the characteristics of mayors are significant predictors of citylevel manipulation, which is contrary to the conventional wisdom. It is widely believed that party secretaries are in charge of party affairs such as personnel while mayors responsibilities lie in the daily operation of the city government. This leads to the conjecture that mayors, especially given their administrative role, are responsible for environmental data collection and its quality. We argue that mayor s characteristics are not predictive of city-level data falsification behavior because they are not the most powerful official in the city. A mayor is unlikely to engage in data manipulation without the implicit consent of the party secretary, which makes the characteristics of party secretaries rather than mayors significant predictors of data manipulation. We find that higher proportions of manipulation are positively correlated with economic growth within a city after controlling for general time trends when an elite educated party secretary is in power, whereas they are slightly negatively correlated under other party secretaries. On an annual basis, Chinese city leaders are evaluated according to a predetermined set of performance indicators including economic growth, social stability, and environmental targets. Economic growth is associated with higher pollution levels, and it is the ultimate target for local officials. Local officials are reluctant to protect the environment by slowing down economic growth. Our result is consistent with the conjecture that these city party secretaries prioritize economic growth at the cost of honestly meeting the environmental targets. Our research contributes to several strands of literature. First, it suggests that meritocratic promotions might create unintended consequences in terms of data manipulation. Economists argue that the Chinese central government uses the incentive of career advancement to induce desirable economic and political outcomes for local officials (Li and 4

5 Zhou, 2005; Xu, 2011). However, a growing body of literature raises the concern whether meritocratic promotions can select competent leaders (Ghanem and Zhang, 2014; Jia et al., 2015; Fisman and Wang, 2017). We add to the literature that some officials characteristics that matter for promotion are also significant predictors for manipulation. In particular, because education level is an important determinant of promotion (Shih et al., 2012), those officials with elite-college status are more likely to manipulate data because their marginal benefit of manipulation is greater. Second, our research challenges the conventional wisdom that only mayors are responsible for environmental protection. For example, Zheng et al. (2013) find that GDP growth is the only significant predictor of the probability of promotion for party secretaries, whereas mayors promotion is predicted by both economic growth and pollution reductions. Jia (2017) similarly finds that pollution tends to increase during the tenure of mayors who are better connected to the Politburo Standing Committee, whereas the empirical evidence did not strongly support similar findings for party secretaries. However, we find that elite-college educated party secretaries are more likely to manipulate the data because of promotion incentives. Third, the econometric method we propose to estimate the proportion of data manipulation is general and can be applied in any setting where there is a policy cut-off that incentivizes manipulation. For example, the proposed method could be used to uncover the degree of test score manipulation in high-stakes testing in schools (Dee et al., 2011; Diamond and Persson, 2016; Figlio, 2006; Figlio and Getzler, 2002; Jacob, 2005; Reback and Cullen, 2006), or to estimate the proportion of manipulation in the running variable in any regression discontinuity design. Our model can also be extended to estimate the proportion of excess bunching, such as in the literature on behavioral responses to tax policy (Saez, 2010; Chetty et al., 2011). 5

6 One limitation of the empirical analysis in this paper is that our identification strategy only captures manipulation around the cutoff of blue-sky days. There is anecdotal evidence that city governments use other methods to ameliorate the readings of air pollutant concentrations, such as choosing favorable locations for weather stations or taking measures to reduce pollutant concentrations around existing weather stations year round. Our method does not take such systematic manipulation behavior into account. The paper is organized as follows. Section 2 and 3 describe the institutional background and the data, respectively. Section 4 outlines the identification strategy of the proportion of manipulation and presents the relevant estimation results. Section 5 presents the results on the relationship between local official characteristics and manipulation behavior. Section 6 concludes. 2. Institutional Background China s local political system is intricate, not only because each city is ruled by a party secretary and a mayor, but also because the division of labor between them and their relative power is complex. One of the key determinants of a local official s position in the political hierarchy is the administrative ranking of the city where he/she is posted. The four direct-controlled municipalities - including Beijing, Shanghai, Tianjin, and Chongqing - are province-level cities. They are followed by 15 sub-provincial cities, mostly provincial capitals. The vast majority of cities are at the prefecture level. There are also county-level cities, but they are not in our sample of analysis. 1 In order to understand a government official s ranking in the political system, we must take other political affiliations of the official into account, in addition to the administrative ranking of the city in question. Although a city party secretary and a mayor are posted at the same administrative level, their political affiliations can be different. In most cases, the party secretary has superior political affiliations to the mayor in the same city. The party secretary of a 1 Note that in China, counties are lower in administrative ranking than cities. 6

7 direct-controlled municipality is usually a politburo member, which is at the sub-national level, while the mayor remains at the provincial level. For a provincial capital city or a city with economic importance, its party secretary is usually a standing member of the provincial party committee, which is at the sub-provincial level, while its mayor is still at the prefecture level. In these cases, the party secretaries strictly dominate the mayor in political rank. In most prefecture-level cities, party secretaries and mayors have the same administrative and political rank. However, it does not imply that they share power equally. Only in very rare cases, the mayor plays an equal or even dominant role in local politics. In general, however, since the party has unequivocal leadership in Chinese politics, the party secretary is the top leader of a city and has full control over the local government. The mayor is often a deputy party secretary and hence reports to the party secretary. Furthermore, during , many party secretaries were also heading the local People s Congress, which is typically in charge of appointing the mayor of the city. In addition, even though the Chinese political system has a built-in mechanism of mutual supervision, the local officials are all subordinates to the party secretary. The integrity of the party secretary relies mainly on self-supervision. Therefore, the party secretary has absolute local power in both party and government without an effective supervision and control mechanism. Furthermore, the party secretary determines the promotion of most local officials. He/she can also influence the appointment of new local officials. For instance, although the mayor is appointed by higher-level government officials, the recommendation of the party secretary is very important in the decision process. To summarize, although Chinese cities use the dual-head system for power balance, the party secretary of a city is the dominant local leader. It is important to understand the promotion of local officials in China, which is determined by a complex set of factors. The official guiding principles of promotion are 7

8 best described in the Comprehensive Assessment and Evaluation Methods for Local Party and Government Leaders (2009 No. 13) published by the Organization Department of the Chinese Communist Party. The document stipulates that the assessment is based on five categories: ideological and political construction, leadership, work performance, anticorruption, and compliance with the key objectives and tasks. In terms of key performance indicators, local officials are assessed according to the following criteria: economic development, social development, and sustainable development. Environmental protection and emissions reduction is listed as a key indicator subject to annual assessment for both party secretaries and mayors. 3. Data We construct a unique city-level panel dataset of 111 cities between 2001 and 2010, which includes party secretary and mayor characteristics, annual economic data, as well as annual measures of manipulation of blue-sky days, which is constructed using our daily air quality variables. A list of cities in our data set is given in Table A Party Secretary and Mayor Data. We construct a detailed data set of demographic, education and work experience variables for all party secretaries and mayors that held office in the 111 cities in our data set between , subject to data availability. To the best of our knowledge, this is the first data set to have such detailed information on local officials in China. Table 1 presents the summary statistics of the baseline variables in our data set. 2 Those variables are constructed from a raw data set that we manually collected. Both the cleaned and raw datasets are available upon request. The names of the variables in the raw data set are given in Table A2. 2 Since we do not have unique identifiers for local officials in our data set, we collapse our data by the name of the local official and city. We have 313 observations for party secretaries and 330 for mayors. It is possible that some party secretaries and mayors serve in the city with the same post. For party secretaries, there are at most 14 such occurrences in our data set. To account for that, as well as any other correlation in local official selection at the city level, we cluster our standard errors by city when performing hypothesis testing in our empirical analysis. 8

9 The baseline demographic characteristics include gender (male or female) and ethnicity (Han or other). The overwhelming majority of party secretaries in our data set are male Han (88% among party secretaries, 84% among mayors). There are about 3% (2%) female Han and 9% (13%) male non-han among party secretaries (mayors). There are no female non-han in our sample. The education variables include a range of dummy variables for full-time and part-time degrees. For full-time educational degrees, we include dummy variables for college completion (Completed College), STEM majors (STEM Major), and attending an elite college (Elite College), which are highly selective universities in China. Furthermore, we include a dummy variable indicating whether a local official entered college during the Cultural Revolution as a Hong Wei Bing college student (College Entrance During ), since the college admissions criteria were less academic and favored individuals with modest family backgrounds. 3 Similarly, we include a dummy variable that captures whether the local official was among the first two cohorts of college students selected immediately after the Cultural Revolution (College Entrance During ), indicating that the official had a strong academic background before entering college and received a high-quality college education. In our sample, 60% (55%) of party secretaries (mayors) have completed a full-time college degree, 33% (31%) majored in a STEM field, and 27% (25%) attended an elite college. In terms of college entrance, 18% (17%) entered college between and 24% (22%) between For part-time educational degrees, we have two binary variables for whether the local official attended a part-time college regardless of college completion (Part-time College) and whether the local official obtained a part-time graduate degree (Part-time Graduate Degree). Among party secretaries (mayors), 38% (43%) 3 During the Cultural Revolution, the college admission criteria put less emphasis on academic standards and favored students from peasant and working-class families (Chang, 1974). Furthermore, much of the urban youth, who would otherwise enter college, were sent to rural areas to work. Hence, the first two college entrance exams after the Cultural Revolution were arguably the most competitive exams attracting many of those who were not allowed a university education during the Cultural Revolution. 9

10 have completed a part-time college degree or some part-time college, 25% (19%) obtained a part-time graduate degree. The baseline experience variables in our data set fall under three categories: (1) current post, (2) previous experience, and (3) previous locations. For the current post, we include tenure in the current post (Years in Current Post) and years to retirement (Years to Retirement), which is determined by an official s age and the ranking of the city, as well as a dummy for whether the current post is in the official s birth province (Current Post in Birth Province). The average party secretary (mayor) in our sample serves about 2.1 (1.94) years in the current position, and has about 5.53 (7.55) years to retirement. 4 About 58% (59%) of party secretaries (mayors) in our sample are currently posted in their birth province. For previous experience, we include indicator variables for industry experience (Enterprise) and research experience (Research), where the latter includes academic and non-academic research positions. About 41% (46%) of party secretaries (mayors) in our sample had previous enterprise experience, whereas 26% (25%) had previous research experience. Furthermore, we have a host of dummy variables for previous government positions held, Administrator in Government or Party Organization, County Mayor, County Party Secretary, City Mayor, City Party Secretary, and Central Government. Note that counties have a lower administrative ranking than cities in the Chinese system. Among party secretaries (mayors), 30% (37%) had been administrators in government or party organizations, 22% (28%) had served as county mayors, 32% (31%) as county party secretaries, 58% (17%) as city mayors, 28% (9%) as city party secretaries, and 16% (14%) had previous work experience in the central government. Finally, we have indicator variables for whether the current official s previous post was in the current city (Current City), current province (Current Province) or another province (Other Province). Almost everyone in our sample 4 Note that the difference between years to retirement between secretaries and mayors is partly due to the fact that city party secretary is a step above city mayors. Hence city party secretaries on average tend to be older. 10

11 had a previous post in the same province as their current position. An overwhelming majority also had prior posts in the current city (81% among party secretaries, 89% among city mayors). About 27% (23%) of party secretaries (mayors) have served in a different province. In the LASSO regression, we not only include all of the above covariates, but also interactions between all those demographic, education, and experience characteristics Air Pollution and Economic Variables. We use a city-level panel of daily PM 10 concentrations for 111 cities from The data is produced by the China National Environmental Monitoring Center (CNEMC), an affiliate of the Ministry of Environmental Protection of China, and is a mere compilation of the data reported by the city local governments. The PM 10 concentrations are piece-wise linearly transformed into an API index. The PM 10 concentration cut-off corresponding to the blue-sky day is 0.15 parts per billion (ppb). Ghanem and Zhang (2014) have found evidence consistent with manipulation predominantly for PM 10 concentrations, whereas such evidence was found to a much lesser degree for other criteria pollutants used for the construction of the API during the period we examine, SO 2 and NO 2. The economic variables are obtained from the China City Statistical Yearbooks for The summary statistics for PM 10 concentrations as well as the economic variables we consider are given in Table Estimating the Proportion and Number of Manipulated Blue-sky Days 4.1. Econometric Identification. If both reported and true PM 10 concentrations (hereafter PM 10 ) are observed for any given day, then the identification of data manipulation is trivial, since the proportion of data manipulation can be identified by comparing the reported and true PM 10 distributions to the left of the threshold value, as demonstrated in Figure 1. In many empirical settings where misreporting is suspected, the distribution of the true variable is unobserved. Our identification strategy relies on identifying the true 11

12 distribution using knowledge of the cut-off that incentivizes data manipulation as well as a set of assumptions appropriate for our empirical setting. Let X be the reported PM 10 and Z a binary random variable that takes value 1 if the reported index is manipulated and 0 otherwise; X is observed while Z is unknown. Let c be the cut-off such that a day with X c is a reported blue-sky day. The reported PM 10 is a combination of true and manipulated data. That is, X = (1 Z)X(0) + ZX(1), where X(0) is the true PM 10 and X(1) the manipulated PM 10. Let λ = P (Z = 1) be the total proportion of PM 10 manipulation. To establish identification of λ, we make the following assumptions. Assumption 1. Z = 0 if X(0) c. Assumption 2. X(1) c. Assumption 3. P (Z = 1 X = x) = 0 for all x / [ x, x], where c [ x, x] and P ( x X x) < 1. Assumption 4. The cdf of X(0) is G(.; θ), where G(.; θ) is a known function with density g(.; θ) and θ an unknown finite-dimensional parameter. The first two assumptions follow naturally from our empirical setting. Since manipulation that does not switch a non-blue sky day to a blue-sky day carries no benefit to the local official, it is reasonable to assume that no manipulation occurs if the true PM 10 concentration is already below the cut-off for blue-sky days (Assumption 1) and that all manipulation move PM 10 below the cut-off for blue-sky days (Assumption 2 ). To accommodate other empirical settings, one could allow manipulation to occur in the opposite direction. Hence, the key restrictions in these two assumptions are that manipulation is unidirectional, and that the direction of manipulation is known. 12

13 Assumption 3 is more restrictive, but is important to identify our objects of interest. As mentioned above, to identify the proportion of manipulation, we need to identify the distribution of the true PM 10 concentration, or X(0). Assumption 3 imposes that manipulation occurs only within a particular window around the blue-sky day cutoff. This allows us to observe the true concentration, X(0), as a censored variable, specifically = X if X / [ x, x]; X(0) [ x, x] if X [ x, x]. (1) We will refer to [ x, x] as the window of manipulation in the following. Since local governments release the data on a daily basis to its citizens, who may detect large levels of misreporting and consequently protest, this assumption is reasonable for our empirical application. Lastly, Assumption 4 assumes that the class of parametric distributions for the true PM 10 concentration is known. 5 Assumption 4 appears to be restrictive, however, in the empirical analysis, we use a very board class of parametric distributions the generalized beta distribution of the second kind (GB2), which nests a wide range of common distributions such as the generalized gamma, lognormal, Weibull, chi-square, half-normal, exponential, log-logistic, etc. This parametric class of distributions fits the air pollution data very well as we will demonstrate in Section 4.2. Now we illustrate the identification of λ from G(.; θ) and the distribution of the observed PM 10, denoted hereinafter by F X (.). We can decompose the probability of observing 5 Our need to specify the functional form of the distribution of true PM10 is related to Lee and Card (2008) where the specification of a parametric functional form is required to identify the local average treatment effect in the regression discontinuity design for discrete regressors. The key similarity is that in both situations we do not observe the continuous variable in question on its entire support. For the situation in Lee and Card (2008), they observe a discretized version of the continuous variable. For our setup, even if the reported data is continuous, we do not observe the true PM 10 within the window of manipulation. 13

14 reported PM10 below the cut-off value c as follows F X (c) = E[1(X c, Z = 0) + 1(X c, Z = 1) = E[1(X(0) c, Z = 0) + 1(X(1) c, Z = 1)] = E[1(X(0) c)] + E[1(Z = 1)] = G(c; θ) + λ. The second equality follows from the definition of X. The third equality follows from Assumption 1 and 2. Since there is no manipulation below the cutoff c by the former assumption, the event (X(0) c) implies (Z = 0). By the latter assumption, manipulation leads to a reported value that is below the cutoff c. Hence, the event (Z = 1) implies that (X(1) c). The last equality holds following the definition of G(c; θ) and λ. Therefore, the proportion of manipulation is given by λ = F X (c) G(c; θ). Likewise, one can show that the proportion of manipulation among reported blue-sky days satisfies µ = P (Z = 1 X c) = λ F X (c) = F X(c) G(c; θ), F X (c) where the second equality holds by Assumption 1. The above identification result can be extended to settings where incentive structures are continuous but have kinks, such as excess bunching. The relevant result is given in Appendix A. A well-studied example is bunching resulting from kinks in the net-of-tax budget constraints. In that setting, we often observe bunching in the data as opposed to a sharp discontinuity. Chetty et al. (2011) and Saez (2010) use different methods to measure excess bunching. It is important to note here that such behavioral responses incorporate changes in the labor supply and may be present even in the absence of manipulation. For instance, Chetty et al. (2011) find evidence of bunching in their study of Danish tax 14

15 records, even though the probability of manipulation of income is quite low due to thirdparty reporting Results. Since the identification result is based on a parametric distributional assumption of a censored variable, the natural estimator of θ in this context is MLE. For city i in year t, let X itd denote the reported PM 10 concentration on day d. Let G(x, θ it ) be the c.d.f. of the true PM 10 value in city i at year t and g(x, θ it ) be the corresponding p.d.f. As is discussed earlier, we use the GB2 distribution for its flexibility in estimating distributions of positive continuous random variables. The cutoff for blue-sky days is 0.15 for the PM 10 concentration. We set the manipulation window as [ x, x] = [0.135, 0.18]. The censored maximum likelihood estimator is given by the following T it ˆθ it = arg max {1{x itd / [ x, x]} log g(x itd ; θ it ) + 1{x itd [ x, x]} log(g( x; θ it ) G( x; θ it ))}, θ it Θ d=1 where T it is the total number of days observed for city i in year t. Figures 2-3 illustrate the estimated c.d.f. of true P M 10, G(.; ˆθ it ), as well as the empirical c.d.f. of daily reported P M 10, ˆFXit (.), for Beijing, Shanghai, Chongqing and Tianjin, the four provincial-level cities of China. The figures for all other cities in our data set are given in Supplementary Appendix I. The proportion of manipulation among reported blue-sky days for a given city-year combination could then be estimated following ˆµ it = ˆF Xit (c) G(c; ˆθ it ) ˆF Xit (c) where c is the cutoff for blue-sky days, 0.15 ppb. Figures 4-5 plot the proportion of reported vs. predicted blue-sky days as well as the proportion of manipulation among reported blue-sky days for the four provincial-level cities between The figures show the heterogeneity in the evolution of the proportion of manipulation over time. Supplementary Appendix II includes the plots for all other cities in our data set. 15

16 Finally, the total number of manipulated blue-sky days, m it, could be estimated following m it = ( ˆF Xit (c) G(c; ˆθ it )) T it To summarize our results across cities, Figure 6 presents the annual histograms for manipulated blue-sky days across cities. The histograms provide evidence of substantial heterogeneity from year-to-year in the number of blue-sky days that are manipulated by cities. 5. Local Leaders and Environmental Compliance in China 5.1. Econometric Strategy. We are interested in understanding which mayor and party secretary characteristics and interactions thereof are the most important predictors of air quality data manipulation. We have a fairly large number of mayor and party secretary characteristics in our data set, specifically 24 for each mayor and party secretary, i.e. a total of 48 variables. Furthermore, we would like to include interactions of demographic, education and experience variables in our regression. This would yield a very large number of regressors, and it is well-known that regressions with large numbers of covariates lead to spurious results, not to mention the danger of specification searching and p-hacking. We hence apply the LASSO shooting algorithm proposed by Belloni et al. (2014a) to select among the characteristics in our dataset. The main advantage of this procedure is that it delivers valid post-selection inference that is robust to model selection errors. The key assumption of the LASSO method is the approximate sparsity assumption, which means that the relationship between the outcome variable and regressors can be well approximated by a linear function of a small number of regressors. Since we have predominantly binary regressors that we interact, the linearity of the approximating function is not restrictive. Furthermore, the results illustrate that the sparsity assumption is appropriate for our empirical setting. Let z m,j it denote the j th mayor characteristic, and z s,j it denote the j th party secretary characteristic in city i at year t. The LASSO model selection step is implemented on the 16

17 following equation y it = K j=1 β m,j z m,j it + K j=1 β s,j z s,j it + γ p + δ r + λ t + u it, where γ p, δ r and λ t are province, city-rank and year fixed effects, respectively. These fixed effects are treated as control variables in the LASSO procedure and the variable selection is only among the mayor and party secretary characteristics. 6 We implement this procedure twice with the proportion of manipulation among reported blue-sky days (ˆµ it in the previous section) and the total number of manipulated blue-sky days ( ˆm it in the previous section) as the dependent variable. We consider two different variants of the LASSO procedure where we include different regressors for selection: (I) demographic, education and experience variables; (II) demographic, education and experience variables as well as interactions of demographic and education, demographic and experience, and education and experience variables. The post-lasso regression for each variant includes all variables selected in the LASSO selection with either ˆµ it or ˆm it as the dependent variable. 7 We also check the robustness of our post-lasso results to the inclusion of city fixed effects Post-LASSO Results and Robustness Checks. The main finding from our post- LASSO results is that the only variable that is a significant predictor of manipulation within cities is that the party secretary in power has an elite college degree, hereinafter PSEC (Party Secretary Elite College). Table 3 reports the post-lasso results for the two variants of the selection procedure with the proportion of manipulation as the dependent variable as well as the fixed effects versions of the post-lasso regressions, where city fixed effects are also included. We find that having a party secretary with an elite college degree (PSEC=1) is associated with a 1.1% annual increase in the proportion of manipulation 6 For provincial level cities, such as Beijing, including province fixed effects is equivalent to including city fixed effects. 7 The implementation here is similar to post-selection inference in treatment effects models (Belloni et al., 2014b), where selection is performed twice on the outcome variable and the treatment to avoid any omitted variable bias. 17

18 (2.38 manipulated blue-sky days), which is statistically significant at the 5% level. To put this finding into context, the average proportion of manipulation in our sample is 3.1% with a standard deviation of 4.3%. Hence, the average increase in the proportion of manipulation associated with PSEC is about 30% of the sample mean and about 25% of the standard deviation. The relative increases are similar for manipulated blue-sky days. In terms of the robustness checks of our result, first of all, there is no guarantee that the different variants of the LASSO procedure select the same regressors, hence the fact that our two LASSO variants select the same variable suggests that the PSEC variable is robust to the inclusion of interactions in the LASSO variable selection step. We also perform the same LASSO procedures, but without including PSEC among the variables that are available for selection. In this case, no variables are selected, as presented in Table 4. This is another finding that supports that the PSEC variable is the key predictor and is not masking the prediction power of other variables. This is especially important to check, since many of our regressors are binary. One of the surprising aspects of our results is that previous literature (Zheng et al., 2013; Jia, 2017) does not find a strong relationship between career concerns and environmental targets for party secretaries, whereas they find that this connection is important for mayors. This suggests a further robustness check. Specifically, it is possible that the PSEC variable is highly correlated with having a mayor with an elite college degree and hence can confound the predictability of the latter. Table 5 presents the results of fixed effects regression of our two manipulation measures on the Mayor Elite College variable. In both regressions, this variable is not a significant predictor of pollution manipulation. We discuss this finding in the context of career concerns of political officials in China in Section PSEC, Manipulation and Economic Growth. To interpret the LASSO regression results associated with the PSEC variable, we compare party secretaries with an elite college degree with all other party secretaries in Table 6. The difference between the two 18

19 groups along the education variables is not surprising. Elite colleges tend to be oriented toward STEM fields. Furthermore, by definition elite college graduates have to complete their degree as full-time students. They are also less likely to pursue another college degree as a part-time student. Furthermore, the party secretaries that have elite degrees in our sample are twice as likely to have entered college during the Cultural Revolution ( ), specifically this cohort s proportion is 29% among elite college graduates, whereas it is only 14% among other college graduates. Elite education is also significantly correlated with several experience variables. Specifically, elite educated party secretaries are 24% less likely to be currently posted in their birth province, suggesting geographical mobility, better political connection, and higher chance to receive a promotion in the future. In terms of previous work experience, elite educated secretaries are 12% more likely to have previous enterprise as well as research experience, 14% less likely to have served as county mayor, 19% less likely to have served as county party secretary, and 12% more likely to have been posted at the central government. 8 These differences suggest that party secretaries with elite college degrees are less likely to have to climb the promotion ladder from county-level positions to higher level positions, and hence tend to be appointed to city-level positions from outside the province. This further suggests that the PSEC variable encompasses several unobservable characteristics, including ability and connections. In order to understand which of these factors are more likely to be driving the relationship between manipulation and this variable, we explore the heterogeneity in the relationship along these different dimensions. Tables 7-8 present fixed effects regressions of the proportion of manipulation on the PSEC variable as well as its interactions with different party secretary attributes, such as 8 Table A3 presents the same analysis for mayors. The key difference in this comparison is that mayors with and without elite college degrees are not significantly different in terms of college entrance during the cultural revolution, being posted in their birth province as well as previous experience in the central government. However, the former is significantly more likely to have entered college during and to have about two more years to retirement on average. 19

20 gender, college cohort, major, tenure, and retirement as well as different previous experience variables. The only statistically significant result is that among elite educated party secretaries, the female Han are significantly less likely to manipulate relative to the male Han and the male non-han. Regarding previous experience, having research experience also significantly reduces the proportion of manipulation relative to other elite educated party secretaries. Furthermore, prior experience as county mayor also reduces the probability of manipulation. The results with manipulated blue-sky days as the dependent variable presented in Tables 9-10 are similar in terms of the signs of the coefficients, but female Han and previous research experience do not have statistical significance. The above results suggest that it is unlikely that the correlation between the PSEC variable and manipulation is driven by unobservable ability, given that previous research experience is associated with less manipulation. Furthermore, city party secretaries that previously served as county mayors are likely to have climbed the administrative ladder to arrive at the current post. They are less likely to be well connected in the political sphere. Hence, the interpretation of the PSEC variable in the context of manipulation is most consistent with PSEC capturing connections. This is consistent with previous literature (Jia and Li, 2017) that find the wage premium due to elite education in China is most likely associated with connections as well as signaling rather than technical ability. The relationship between the number of high-level government officials by the ranking of their university depicted in Figure 7 further supports this interpretation. It is evident that the future career for a government official is strongly correlated with his/her educational attainment. The lower ranked universities produce a much smaller number of high-level officials. Education has become a glass ceiling for Chinese officials. Those officials who graduated from elite universities are more likely to get promoted. This also suggests that elite universities provide access to a more powerful network through their alumni. For the party secretaries with an elite college status, the likelihood of being promoted is much 20

21 higher than those graduated from ordinary universities. Therefore, these officials have a bigger incentive to beautify their resumés because the marginal benefit of doing so is larger. Finally, we turn to the relationship between economic growth and manipulation of air quality data. Despite the environmental targets set by the central government, economic growth is the primary criterion for promotion, especially for local party secretaries (Zheng et al., 2013). Hence, one potential explanation for our results is that elite educated party secretaries prioritize economic growth in order to achieve their goal of getting promoted. Manipulation around the cut-off for blue-sky days then occurs to ensure that the city meets the minimum number of blue-sky days in a year, to hide the deterioration of air quality resulting from rapid economic growth, or to demonstrate consistent improvement in air quality over the years that the local official is in charge. Panel A of Table 11 presents the mean of economic indicators when an elite educated party secretary is in power and differences relative to other party secretaries. The former tend to be placed in cities with larger GDP, especially in the secondary and tertiary sectors. However, the differences are not statistically significant. Panel B of Table 11 also presents the within-city correlation between the proportion of manipulation and GDP after accounting for time fixed effects. To compute these correlations, we first remove city-specific and time-specific unobservables from both the proportion of manipulation (Z 1 it = αi 1 + λ1 t + u 1 it ) and the economic variable in question (Z 2 it = α2 i + λ2 t + u 2 it ). The correlation given in the table is the correlation between u1 it and u 2 it. Hence, it is neither confounded by city-specific unobservables nor general trends in manipulation or GDP. We find that conditional on having an elite educated city party secretary (P SEC = 1), the within-city correlation between GDP and the proportion of manipulation is positive, 0.11, whereas it is when other college educated secretaries are in power (P SEC = 0). When we look at GDP by sector, we find similar correlation patterns for the secondary and tertiary sector, whereas the correlation is negative for the 21

22 primary sector conditional on P SEC = 1 and positive conditional on P SEC = 0. The results are intuitive since the primary sector is not a major contributor to air pollution Discussion. In order to understand our finding regarding the role of party secretaries in the manipulation of air quality data, we further investigate their career concerns and their role in managing cities. In general, a party secretary has a higher incentive for promotion than a mayor, since they are one step closer to moving up in administrative rank. For example, a prefecture-level party secretary can advance to the sub-provincial level in the next promotion. In contrast, a mayor is usually promoted as the local party secretary, which is at the same administrative rank as the mayor in most cities. There is also time pressure to advance in rank, since the Chinese political system has strict requirements on age, and the retirement age is pushed back for officials with higher administrative rank. The career of a government official is hence shadowed if he/she cannot advance to a particular level by a certain age. A party secretary also faces much tougher competition in the promotion tournament. Most mayors can be promoted to a party secretary position in the same or different jurisdiction. The career advancement is almost certain following the retirement or career advancement of the party secretary. In comparison, the promotion of a party secretary is more uncertain, especially as he/she advances in rank. There are simply much fewer positions at the higher levels. Party secretaries must compete with their counterparts in other jurisdictions to get promoted. It is unlikely that the promotion is only determined by the rank of performance indicators, aka the meritocracy model. The higher-level party leader has the power to decide whom to promote. Therefore, local officials are not working to maximize some index to be ranked higher. Instead, they strive to achieve satisfying scores for every single category to qualify for a promotion. The leadership of the communist party is best illustrated by controlling the personnel instead of relying on a promotion formula. The indicators help 22

23 the party identify those qualified candidates but the final selection is based on political considerations. A local official with an obvious weakness, e.g. non-compliance with environmental standards, risks being found ineligible for promotion. Therefore, although environmental performance is arguably a less important indicator compared with GDP, local officials strive to comply with the environmental targets because pollution can be a stain on their resumé. The party secretary sets the overall strategy of economic development and environmental protection, which is then implemented by the mayor. For a city to achieve the economic target set by the higher-level government while complying with environmental standards, the only option is to manipulate the data, at least in the short run. Although a party secretary is unlikely to instruct local government officials directly to falsify the data, the pressure of environmental compliance without resource commitment may force the officials in local environmental protection bureaus to do so. Because the promotion of most city officials is subject to the party secretary, local officials will cave in to meet the implicit demand of the local leader. This suggests that the party secretary has a critical, though implicit role in condoning the manipulation of air quality data. Furthermore, a mayor cannot engage in data manipulation without the implicit permission of the party secretary, otherwise the mayor risks being subjected to disciplinary action. On the other hand, a mayor has little or no incentive to stop the party secretary from allowing data manipulation because the mayor will also benefit from a beautiful local performance record. The promotion of the party secretary vacates the top leadership position for the mayor. All of the above sheds light on our finding that data manipulation is largely determined by city party secretaries attributes rather than mayors attributes. 6. Conclusion China has been using performance evaluation and promotion incentive to induce local officials to comply with the goals and targets set by the central government. This institution 23

24 partially explains China s rapid economic growth in the past forty years. However, this approach also creates some unintended consequences, including falsification of the data used to evaluate local officials. Using air pollution data manipulation as an example, this paper illustrates how local officials might respond to the incentive by cheating instead of exerting effort. Specifically, we propose in this paper an innovative method to quantify the extent of manipulation of air quality data and then determine its key predictors among a large number of characteristics of city party secretaries and mayors. We find that the key predictive factor of city-level data manipulation is to have a party secretary in power with an elite college degree. This is likely due to the fact that the elite college educated party secretaries have a higher chance of future promotion. When environmental compliance is at conflict with GDP growth, those who have most to gain from an un-stained compliance record are more likely to turn a blind eye with regards to local data manipulation. This result challenges the theory that meritocratic promotions can select competent leaders. 24

25 Appendix A. Identification of Excess Bunching The key difference between the data bunching problem studied in, for example, Saez (2010), and the data manipulation problem studied in Section 4.1 of the paper is that in data bunching the movement of data, although still unidirectional, is not restricted to shifting data from the right of the cut-off c to the left of the cut-off. Specifically, a kink in tax policy, say, increased marginal tax rate after cut-off value c may incentivize workers to reduce their work efforts, or to misreport their earnings when filing their tax returns. But many taxpayers, as is reasoned in Saez (2010), are unable to perfectly control their incomes or may not be aware of the exact location of kink points. Therefore, researchers typically observe a bunching pattern in data around the cutoff c as opposed to a discontinuity in density in the data manipulation case. For the data bunching problem, we replace both Assumption 1 and 2 in Section 4.1 with the following Assumption. Assumption 5. X(1) c, where c ( x, x), f X ( c) = g( c; θ) and f X (x) g(x; θ) for all x ( x, x)\ c. Since the probability mass in data bunching is not simply moving from the right to the left of the cutoff, we can no longer identify the probability of manipulation, or λ, from F X (c) G(c; θ). Instead, we can identify the proportion of individuals responding to the tax policy using the following quantity λ b = x x (f X (x) g(x; θ)) + dx, where h(x) + = h(x)1{h(x) > 0}, f X (x) is the probability density function of the observed data with progressive tax schedule and g(x; θ) is probability density function of the hypothetical situation with constant marginal tax rate. Note that f X (x) could be estimated by nonparametric kernel density estimation and g(x; θ) could be estimated by parametric 25

26 censored MLE following the discussions in Section 4.1. If Assumption 5 does not hold, then we can only identify a lower bound for λ b. References Andrews, Steven Q., Beijing Plays Air Quality Games, Far Eastern Economic Review, Andrews, Steven Q, Inconsistencies in air quality metrics: Blue Sky days and PM 10 concentrations in Beijing, Environmental Research Letters, 2008, 3 (3), Belloni, Alexandre, Victor Chernozhukov, and Christian Hansen, High- Dimensional Methods and Inference on Structural and Treatment Effects, Journal of Economic Perspectives, 2014, 28 (2), ,, and, Inference on Treatment Effects after Selection among High-Dimensional Controls, The Review of Economic Studies, 2014, 81 (2), Chang, Parris H., THE CULTURAL REVOLUTION AND CHINESE HIGHER EDU- CATION: CHANGE AND CONTROVERSY, The Journal of General Education, 1974, 26 (3), Chen, Yuyu, Ginger Zhe Jin, Naresh Kumar, and Guang Shi, Gaming in Air Pollution Data? Lessons from China, The B.E. Journal of Economic Analysis & Policy (Advances), 2012, 13 (3), Article 2. Chetty, Raj, John N. Friedman, Tore Olsen, and Luigi Pistaferri, ADJUST- MENT COSTS, FIRM RESPONSES, AND MICRO VS. MACRO LABOR SUPPLY ELASTICITIES: EVIDENCE FROM DANISH TAX RECORDS, The Quarterly Journal of Economics, 2011, 126 (2), pp Dee, Thomas, Brian Jacob, Justin McCrary, and Jonah Rockoff, Rules and discretion in the evaluation of students and schools: The case of the New York regents examinations, Central for Education Policy Analysis Working Paper, Stanford University,

27 Diamond, Rebecca and Petra Persson, The Long-term Consequences of Teacher Discretion in Grading of High-Stakes Tests, NBER Working Paper 22207, Figlio, David, Testing, crime and punishment, Journal of Public Economics, 2006, 90, and Lawrence Getzler, Accountability, Ability and Disability: Gaming the System, NBER Working Paper No. 9307, Fisman, Raymond and Yongxiang Wang, The Distortionary Effects of Incentives in Government: Evidence from China s, American Economic Journal: Applied Economics, 2017, 9 (2), Fu, Qiuzi, Zhongnan Fang, Sofia B. Villas-Boas, and George Judge, An Investigation of the Quality of Air Data in Beijing, Ghanem, Dalia and Junjie Zhang, Effortless Perfection: Do Chinese cities manipulate air pollution data?, Journal of Environmental Economics and Management, 2014, 68 (2), Jacob, Brian, Accountability, incentives and behavior: the impact of high-stakes testing in the Chicago Public Schools, Journal of Public Economics, 2005, 89 (5-6), Jia, Ruixue, Pollution for Promotion, Unpublished Manuscript. and Hongbin Li, Access to Elite Education, Wage Premium, and Social Mobility: The Truth and Illusion of China s College Entrance Exam, Unpublished Manuscript., Masayuki Kudamatsu, and David Seim, Political selection in China: The complementary roles of connections and performance, Journal of the European Economic Association, 2015, 13 (4), Lee, David S. and David Card, Regression discontinuity inference with specification error, Journal of Econometrics, 2008, 142 (2), The regression discontinuity design: Theory and applications. 27

28 Li, Hongbin and Li-An Zhou, Political turnover and economic performance: the incentive role of personnel control in China, Journal of Public Economics, 2005, 89 (910), McCrary, Justin, Manipulation of the running variable in the regression discontinuity design: A density test, Journal of Econometrics, February 2008, 142 (2), Reback, Randall and Julie Berry Cullen, Tinkering toward accolades: School gaming under a performance accountability system, NBER Working Papers 12286, Saez, Emmanuel, Do Taxpayers Bunch at Kink Points?, American Economic Journal: Economic Policy, 2010, 2 (3), Shih, Victor, Christopher Adolph, and Mingxing Liu, Getting Ahead in the Communist Party: Explaining the Advancement of Central Committee Members in China, American Political Science Review, 2012, 106, Xu, Chenggang, The fundamental institutions of China s reforms and development, Journal of Economic Literature, 2011, 49 (4), Zheng, Siqi, Mathew Kahn, Weizeng Sun, and Danglun Luo, Incentivizing China s Urban Mayors to Mitigate Pollution Externalities: The Role of the Central Government and Public Environmentalism, NBER Working Paper No

29 Table 1. Party Secretary and Mayor Characteristics: Summary Statistics Sample Mean (S.D.) Secretary Mayor Demographic Characteristics Male Han Female Han Male non-han Education Full-time Completed College STEM Major Elite College Entered College Between Entered College Between Part-time College Graduate Degree Experience Years in Current Post (1.41) (1.50) Years to Retirement (3.74) (4.40) Current Post in Birth Province Previous Experience Enterprise Research Administrator in Gov t or Party Organization County Mayor County Party Secretary City Mayor City Party Secretary Central Government Location of Previous Posts Current City Current Province Other Province # Observations Notes: The above lists the total number of observations we have. Due to data availability, we have some missing observations for some variables. However, the smallest number of observations we have for any of any variable is 304 for party secretaries and 311 for mayors.

30 Table 2. Pollution and Economic Variables: Summary Statistics Mean S.D. Min Max PM 10 concentration GDP (100 million yuan) 1, , , GDP by Sector (100 million yuan) Primary Secondary , Tertiary , , # Observations 983

31 Figure 1. Discontinuity vs. Proportion of Manipulation Panel A. Panel B. Notes: Panel A and B give two situations where the proportion of manipulation is the same, but the pattern of manipulation, i.e. which parts of the support of X and by how much they are manipulated. Specifically, in the top panel, the pattern of manipulation is symmetric around the cutoff, whereas in the bottom panel it is not. The thick blue line is the observed distribution of X, the shaded red area on either left or right side of the cutoff is proportion of manipulation. As a result, the vertical distance between the left and right limit at the discontinuity, which is used in McCrary (2008) is not the same due to the different pattern of manipulation, even though the proportion of manipulation is identical.

32 Figure 2. Empirical vs. Censored MLE PM 10 Distribution: Beijing and Shanghai Beijing: 2004 Beijing: ECDF Para-CDF ECDF Para-CDF Beijing: 2008 Beijing: ECDF Para-CDF ECDF Para-CDF Shanghai: 2004 Shanghai: ECDF Para-CDF ECDF Para-CDF Shanghai: 2008 Shanghai: ECDF Para-CDF ECDF Para-CDF Notes: ECDF denotes the empirical CDF of the reported PM 10 concentrations, whereas P ara CDF denotes our parametrica CDF estimate of the true PM 10 concentrations, which is estimated using using the censored MLE procedure. The vertical green line marks the cut-off for blue-sky days (0.15ppb).

33 Figure 3. Empirical vs. Censored MLE PM 10 Distribution: Chongqing and Tianjin Chongqing: 2004 Chongqing: ECDF Para-CDF ECDF Para-CDF Chongqing: 2008 Chongqing: ECDF Para-CDF ECDF Para-CDF Tianjin: 2004 Tianjin: ECDF Para-CDF ECDF Para-CDF Tianjin: 2008 Tianjin: ECDF Para-CDF ECDF Para-CDF Notes: ECDF denotes the empirical CDF of the reported PM 10 concentrations, whereas P ara CDF denotes our parametrica CDF estimate of the true PM 10 concentrations, which is estimated using using the censored MLE procedure. The vertical green line marks the cut-off for blue-sky days (0.15ppb).

34 Figure 4. Reported vs. Estimated Blue-sky Days: Beijing and Shanghai Beijing: % of Blue-sky Days Beijing: % Manipulation Among Blue-sky Days Year Year Reported Estimated Estimated % 90% Familywise CI Shanghai: % of Blue-sky Days Shanghai: % Manipulation Among Blue-sky Days Year Year Reported Estimated Estimated % 90% Familywise CI Notes: For each city, the left panel plots the time series of the proportion of blue-sky days in a year using the reported PM 10 concentration data and the estimated true PM 10 concentration distribution. The right panel plots the time series of the estimated proportion of manipulation among blue-sky days in a year. The 90% family-wise confidence intervals are calculated using a non-overlapping block-bootstrap procedure with 7-day block length and 100 bootstrap replications.

35 Figure 5. Manipulated vs. Reported Blue-sky Days and the Proportion of Manipulation: Chongqing and Tianjin Chongqing: % of Blue-sky Days Chongqing: % Manipulation Among Blue-sky Days Year Year Reported Estimated Estimated % 90% Familywise CI Tianjin: % of Blue-sky Days Tianjin: % Manipulation Among Blue-sky Days Year Year Reported Estimated Estimated % 90% Familywise CI Notes: For each city, the left panel plots the time series of the proportion of blue-sky days in a year using the reported PM 10 concentration data and the estimated true PM 10 concentration distribution. The right panel plots the time series of the estimated proportion of manipulation among blue-sky days in a year. The 90% family-wise confidence intervals are calculated using a non-overlapping block-bootstrap procedure with 7-day block length and 100 bootstrap replications.

36 Figure 6. Annual Histograms of the Number of Manipulated Days by Year Frequency # of Manipulated Blue-Sky Days Frequency # of Manipulated Blue-Sky Days Frequency # of Manipulated Blue-Sky Days Frequency # of Manipulated Blue-Sky Days Frequency # of Manipulated Blue-Sky Days Frequency # of Manipulated Blue-Sky Days Frequency # of Manipulated Blue-Sky Days Frequency # of Manipulated Blue-Sky Days Frequency # of Manipulated Blue-Sky Days Frequency # of Manipulated Blue-Sky Days Notes: Since we estimate the number of manipulated blue-sky days without imposing a non-negativity constraint, we obtain a few negative, but statistically insignificant, estimates. In the above histograms, we only includes cities with non-negative estimates of the number of manipulated blue-sky days.

37 Figure 7. University Ranking and High-level Politicians Notes: The relationship between number of high-level politicians and university ranking presented. The high-level politicians include the Chinese Communist Party Central Committee members and candidates and the Central Disciplinary Committee members in the 14th-18th party congress. They also include national, sub-national, and minster level politicians since The universities are ranked by China University Alumni Network. The ranked universities are also categorized into 6 grades, with 6 being the highest rank. The data source is